R/Sedentary_summary.R
In YukunZhang/PAactivPAL: Summarize Daily Physical Activity from 'activPAL' Accelerometer Data
Sedentary_summary=function (final_dat,bed_time,takeoff_time) {
options(digits = 10)
if(missing(takeoff_time)){
final_dat=clean_time(final_dat)}else{
final_dat=clean_time(final_dat,takeoff_time)
}
if(missing(bed_time)){
table1=c()
# record.getup<-bed_time
# ###
# record.getup.time<-c()
# record.sleep.time<-c()
# for (kk in 1:nrow(record.getup))
# {
# temp.getup.time<-as.numeric(as.POSIXlt(strptime(as.character(paste(as.character(record.getup[kk,3] ),as.character(record.getup[kk,4]))),"%m/%d/%Y %H:%M:%S"))+2209136400)/24/60/60
# record.getup.time<-c(record.getup.time,temp.getup.time)
# temp.sleep.time<-as.numeric(as.POSIXlt(strptime(as.character(paste(as.character(record.getup[kk,5] ),as.character(record.getup[kk,6]))),"%m/%d/%Y %H:%M:%S"))+2209136400)/24/60/60
# record.sleep.time<-c(record.sleep.time,temp.sleep.time)
# }
###################################################
for (i in unique(final_dat$new_date)){
temp_mat_oneday=final_dat[final_dat$new_date==i,]
# temp_mat_oneday<-temp_mat_oneday[temp_mat_oneday[,1]>record.getup.time[ll] &temp_mat_oneday[,1]<record.sleep.time[ll], ]
time_char<-as.POSIXlt(i*24*60*60, origin = ISOdatetime(1899,12,30,0,0,0))
month<-as.numeric(format(time_char,"%m"))
day<-as.numeric(format(time_char,"%d"))
year<-as.numeric(format(time_char,"%Y"))
dayofweek<-as.numeric(format(time_char,"%w"))
weekday_or_weekend<- ifelse( dayofweek!=0 & dayofweek!=6,1,0)
hour_char<-as.numeric(format(as.POSIXlt(temp_mat_oneday$date_time*24*60*60, origin = ISOdatetime(1899,12,30,0,0,0)),"%H"))
temp_sed<-subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0)$Interval
length_temp_sed<-length(temp_sed)
num_changes_from_sed_to_non_sed<- length_temp_sed
sed_hour<- sum(temp_sed) /60/60
length_temp_sed<-length(temp_sed)
num_changes_from_sed_to_non_sed<- length_temp_sed
total_sed_time<-sed_hour
#total_number_of_sed_bouts<-num_changes_from_sed_to_non_sed
mean_sed_bout_length<- mean(temp_sed) /60/60
prop_of_sed_time_greater_20min<- 100*length(temp_sed[temp_sed>20*60])/length_temp_sed
prop_of_sed_time_greater_60min<- 100*length(temp_sed[temp_sed>60*60])/length_temp_sed
prop_of_sed_time_greater_120min<- 100*length(temp_sed[temp_sed>120*60])/length_temp_sed
total_sed_time_greater_20min<- sum(temp_sed[temp_sed>20*60])/60/60
total_sed_time_greater_60min<- sum(temp_sed[temp_sed>60*60])/60/60
total_sed_time_greater_120min<- sum(temp_sed[temp_sed>120*60])/60/60
sed_time_in_30_and_60=sum(temp_sed[temp_sed>=30*60 & temp_sed<=60*60])/60
sed_num_in_30_and_60=length(temp_sed[temp_sed>=30*60 & temp_sed<=60*60])
quantile_temp<-quantile(temp_sed, probs = c(0.05,0.25,0.5,0.75,0.95))/60/60
percentile_sed_time_5<- quantile_temp[1]
percentile_sed_time_25<- quantile_temp[2]
percentile_sed_time_50<- quantile_temp[3]
percentile_sed_time_75<- quantile_temp[4]
percentile_sed_time_95<- quantile_temp[5]
alpha_sed<- 1+ 1/mean(log(temp_sed/ min(temp_sed)))
#gini_index_sed<- gini(temp_sed)
prop_sed_time_6_12<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=6 & hour_char<12)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=6 & hour_char<12)$Interval)+0.0001) ###prevent this value is zero
prop_sed_time_12_18<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=12 & hour_char<18)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=12 & hour_char<18)$Interval)+0.0001)
prop_sed_time_18_22<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=18 & hour_char<22)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=18 & hour_char<22)$Interval)+0.0001)
break_per_day<-num_changes_from_sed_to_non_sed
break_rate<-break_per_day/sed_hour
table<- cbind(year,month,day,dayofweek,weekday_or_weekend,break_per_day,break_rate,mean_sed_bout_length,prop_of_sed_time_greater_20min,prop_of_sed_time_greater_60min,prop_of_sed_time_greater_120min,total_sed_time_greater_20min,total_sed_time_greater_60min,total_sed_time_greater_120min,percentile_sed_time_5,percentile_sed_time_25,percentile_sed_time_50,percentile_sed_time_75,percentile_sed_time_95,alpha_sed,prop_sed_time_6_12,prop_sed_time_12_18,prop_sed_time_18_22,sed_time_in_30_and_60,sed_num_in_30_and_60)
row.names(table)=NULL
table1=rbind(table1,table)
#print(table1)
}
}else{
table1=c()
record.getup<-bed_time
###
record.getup.time<-c()
record.sleep.time<-c()
for (kk in 1:nrow(record.getup))
{
temp.getup.time<-as.numeric(as.POSIXlt(strptime(as.character(paste(as.character(record.getup[kk,3] ),as.character(record.getup[kk,4]))),"%m/%d/%Y %H:%M:%S"))+2209136400)/24/60/60
record.getup.time<-c(record.getup.time,temp.getup.time)
temp.sleep.time<-as.numeric(as.POSIXlt(strptime(as.character(paste(as.character(record.getup[kk,5] ),as.character(record.getup[kk,6]))),"%m/%d/%Y %H:%M:%S"))+2209136400)/24/60/60
record.sleep.time<-c(record.sleep.time,temp.sleep.time)
}
###################################################
ll=0 #count for getup time
for (i in unique(final_dat$new_date)){
ll=ll+1
temp_mat_oneday=final_dat[final_dat$new_date==i,]
temp_mat_oneday<-temp_mat_oneday[temp_mat_oneday[,1]>record.getup.time[ll] &temp_mat_oneday[,1]<record.sleep.time[ll], ]
time_char<-as.POSIXlt(i*24*60*60, origin = ISOdatetime(1899,12,30,0,0,0))
month<-as.numeric(format(time_char,"%m"))
day<-as.numeric(format(time_char,"%d"))
year<-as.numeric(format(time_char,"%Y"))
dayofweek<-as.numeric(format(time_char,"%w"))
weekday_or_weekend<- ifelse( dayofweek!=0 & dayofweek!=6,1,0)
hour_char<-as.numeric(format(as.POSIXlt(temp_mat_oneday$date_time*24*60*60, origin = ISOdatetime(1899,12,30,0,0,0)),"%H"))
temp_sed<-subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0)$Interval
length_temp_sed<-length(temp_sed)
num_changes_from_sed_to_non_sed<- length_temp_sed
sed_hour<- sum(temp_sed) /60/60
length_temp_sed<-length(temp_sed)
num_changes_from_sed_to_non_sed<- length_temp_sed
total_sed_time<-sed_hour
#total_number_of_sed_bouts<-num_changes_from_sed_to_non_sed
mean_sed_bout_length<- mean(temp_sed) /60/60
prop_of_sed_time_greater_20min<- 100*length(temp_sed[temp_sed>20*60])/length_temp_sed
prop_of_sed_time_greater_60min<- 100*length(temp_sed[temp_sed>60*60])/length_temp_sed
prop_of_sed_time_greater_120min<- 100*length(temp_sed[temp_sed>120*60])/length_temp_sed
total_sed_time_greater_20min<- sum(temp_sed[temp_sed>20*60])/60/60
total_sed_time_greater_60min<- sum(temp_sed[temp_sed>60*60])/60/60
total_sed_time_greater_120min<- sum(temp_sed[temp_sed>120*60])/60/60
sed_time_in_30_and_60=sum(temp_sed[temp_sed>=30*60 & temp_sed<=60*60])/60
sed_num_in_30_and_60=length(temp_sed[temp_sed>=30*60 & temp_sed<=60*60])
quantile_temp<-quantile(temp_sed, probs = c(0.05,0.25,0.5,0.75,0.95))/60/60
percentile_sed_time_5<- quantile_temp[1]
percentile_sed_time_25<- quantile_temp[2]
percentile_sed_time_50<- quantile_temp[3]
percentile_sed_time_75<- quantile_temp[4]
percentile_sed_time_95<- quantile_temp[5]
alpha_sed<- 1+ 1/mean(log(temp_sed/ min(temp_sed)))
#gini_index_sed<- gini(temp_sed)
prop_sed_time_6_12<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=6 & hour_char<12)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=6 & hour_char<12)$Interval)+0.0001) ###prevent this value is zero
prop_sed_time_12_18<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=12 & hour_char<18)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=12 & hour_char<18)$Interval)+0.0001)
prop_sed_time_18_22<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=18 & hour_char<22)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=18 & hour_char<22)$Interval)+0.0001)
break_per_day<-num_changes_from_sed_to_non_sed
break_rate<-break_per_day/sed_hour
table<- cbind(year,month,day,dayofweek,weekday_or_weekend,break_per_day,break_rate,mean_sed_bout_length,prop_of_sed_time_greater_20min,prop_of_sed_time_greater_60min,prop_of_sed_time_greater_120min,total_sed_time_greater_20min,total_sed_time_greater_60min,total_sed_time_greater_120min,percentile_sed_time_5,percentile_sed_time_25,percentile_sed_time_50,percentile_sed_time_75,percentile_sed_time_95,alpha_sed,prop_sed_time_6_12,prop_sed_time_12_18,prop_sed_time_18_22,sed_time_in_30_and_60,sed_num_in_30_and_60)
row.names(table)=NULL
table1=rbind(table1,table)
#print(table1)
}
}
#colnames(table1)<- c("year","month","day","dayofweek","weekday_or_weekend","break_per_day","break_rate","mean_sed_bout_length" ,"prop_of_sed_time_greater_20min","prop_of_sed_time_greater_60min","prop_of_sed_time_greater_120min","total_sed_time_greater_20min","total_sed_time_greater_60min","total_sed_time_greater_120min","percentile_sed_time_5","percentile_sed_time_25","percentile_sed_time_50","percentile_sed_time_75","percentile_sed_time_95","alpha_sed","prop_sed_time_6_12","prop_sed_time_12_18","prop_sed_time_18_22","sed_time_in_30_and_60","sed_num_in_30_and_60")
list(year=table1[,1],month=table1[,2], day=table1[,3],dayofweek=table1[,4],weekday_or_weekend=table1[,5], break_per_day=table1[,6],break_rate=table1[,7], mean_sed_bout_length=table1[,8], prop_of_sed_time_greater_20min=table1[,9], prop_of_sed_time_greater_60min=table1[,10],prop_of_sed_time_greater_120min=table1[,11],total_sed_time_greater_20min=table1[,12],total_sed_time_greater_60min=table1[,13],total_sed_time_greater_120min=table1[,14],percentile_sed_time_5=table1[,15],percentile_sed_time_25=table1[,16],percentile_sed_time_50=table1[,17],percentile_sed_time_75=table1[,18],percentile_sed_time_95=table1[,19],alpha_sed=table1[,20],prop_sed_time_6_12=table1[,21],prop_sed_time_12_18=table1[,22],prop_sed_time_18_22=table1[,23],sed_time_in_30_and_60=table1[,24],sed_num_in_30_and_60=table1[,25])
# out=list( total_number_of_activity_bouts=total_number_of_activity_bouts,mean_activity_bout_length=mean_activity_bout_length,prop_of_activity_time_greater_5min=prop_of_activity_time_greater_5min,prop_of_activity_time_greater_10min=prop_of_activity_time_greater_10min,prop_of_activity_time_greater_30min=prop_of_activity_time_greater_30min,total_activity_time_greater_5min=total_activity_time_greater_5min,total_activity_time_greater_10min=total_activity_time_greater_10min,total_activity_time_greater_30min=total_activity_time_greater_30min,percentile_activity_time_5=percentile_activity_time_5,percentile_activity_time_25=percentile_activity_time_25,percentile_activity_time_50=percentile_activity_time_50,percentile_activity_time_75=percentile_activity_time_75,percentile_activity_time_95=percentile_activity_time_95,alpha_activity=alpha_activity,stepping_to_standing_ratio=stepping_to_standing_ratio,table=table)
#return(table1)
}
#' Sedentary Physical Activity Summary
#'
#' Summarize sedentary features using proportions and percentiles
#' @param final_dat Raw event file, will be cleaned in this function. Event file is required for this function.
#' @param takeoff_time Take on and off time log, reported by participants. Log is not required for this function.
#' @param bed_time Sleep and wake up time log, reported by participants. Log is not required for this function.
#' @return \code{Year} The calendar year of recorded event
#' @return \code{Month} The calendar month of recorded event
#' @return \code{Day} The calendar day of recorded event
#' @return \code{Dayofweek} The day of that week
#' @return \code{Weekday_or_weekend} The recored event date is a weekday or weekend (0 for weekday, 1 for weekend)
#' @return \code{break_per_day} Number of interrupting sedentary behavior during the given period of time
#' @return \code{break_rate} Rate of interrupting sedentary behavior
#' @return \code{mean_sed_bout_length} Mean sedentary bout length. It is the average of the duration time for all sedentary activities
#' @return \code{prop_of_sed_time_greater_20min} Proportions of sedentary bout greater than 20 minutes
#' @return \code{prop_of_sed_time_greater_60min} Proportions of sedentary bout greater than 60 minutes
#' @return \code{prop_of_sed_time_greater_120min} Proportions of sedentary bout greater than 120 minutes
#' @return \code{total_sed_time_greater_20min} Total sedentary time greater than 20 minutes
#' @return \code{total_sed_time_greater_60min} Total sedentary time greater than 60 minutes
#' @return \code{total_sed_time_greater_120min} Total sedentary time greater than 120 minutes
#' @return \code{percentile_sed_time_5} 5\% Percentile of sedentary time
#' @return \code{percentile_sed_time_25} 25\% Percentile of sedentary time
#' @return \code{percentile_sed_time_50} 50\% Percentile of sedentary time
#' @return \code{percentile_sed_time_75} 75\% Percentile of sedentary time
#' @return \code{percentile_sed_time_95} 95\% Percentile of sedentary time
#' @return \code{alpha_sed} alpha of sedentary time, see details
#' @return \code{prop_sed_time_6_12} Proportions of sedentary time between 6:00-12:00
#' @return \code{prop_sed_time_12_18} Proportions of sedentary time between 12:00-18:00
#' @return \code{prop_sed_time_18_22} Proportions of sedentary time between 18:00-22:00
#' @return \code{sed_time_in_30_and_60} Minutes Spent in Sitting/Lying Bouts (at least 30 and less 60 minutes in duration)
#' @return \code{sed_num_in_30_and_60} Number of in Sitting/Lying Bouts (at least 30 and less 60 minutes in duration)
#' @details Proportions of sedentary bout greater than 20/60/120 minutes is the ratio of the number of sedentary bouts greater than 20/60/120 minutes to the total number of sedentary recordings.
#' @details Total sedentary time greater than 20/60/120 minutes is the summation of the sedentary durations which are greater than 20/60/120 minutes.
#' @details To calculate 5\%/25\%/75\%/95\% percentile of sedentary time, all of the recorded sedentary durations are listed and R function \emph{quantile} is used to find the percentiles.
#' @details alpha_sed is defined by \code{1+1/M}, where \code{M} is the average of \code{log(sedentary bout length /minimum sedentary bout length)}.
#' @details Proportions of sedentary time between 6:00-12:00/12:00-18:00/18:00-22:00 is the ratio of the sedentary durations to the total activity durations between 6:00-12:00/12:00-18:00/18:00-22:00.
#' @importFrom stats quantile
#' @examples
#' \donttest{r2=Sedentary(sample_event,sample_bed_time,sample_takeon_log)}
#' \donttest{summary(r2)}
#' @export
#'
Sedentary=function(final_dat,bed_time,takeoff_time) UseMethod("Sedentary")
#' @export
Sedentary.default=function(final_dat,bed_time,takeoff_time)
{
out=Sedentary_summary(final_dat,bed_time,takeoff_time)
out$call=match.call()
class(out)="Sedentary"
out
}
#' @export
print.Sedentary=function(x,...)
{cat("Call:\n")
print(x$call)
}
#' @export
summary.Sedentary=function(object,...)
{
TAB=cbind(object$year,object$month,object$day,object$dayofweek,object$weekday_or_weekend,object$break_per_day,object$break_rate,object$mean_sed_bout_length,object$prop_of_sed_time_greater_20min,object$prop_of_sed_time_greater_60min,object$prop_of_sed_time_greater_120min,object$total_sed_time_greater_20min,object$total_sed_time_greater_60min,object$total_sed_time_greater_120min,object$percentile_sed_time_5,object$percentile_sed_time_25,object$percentile_sed_time_50,object$percentile_sed_time_75,object$percentile_sed_time_95,object$alpha_sed,object$prop_sed_time_6_12,object$prop_sed_time_12_18,object$prop_sed_time_18_22,object$sed_time_in_30_and_60,object$sed_num_in_30_and_60)
colnames(TAB)=c("Year","Month","Day","Dayofweek","Weekday_or_weekend","break_per_day","break_rate","mean_sed_bout_length" ,"prop_of_sed_time_greater_20min","prop_of_sed_time_greater_60min","prop_of_sed_time_greater_120min","total_sed_time_greater_20min","total_sed_time_greater_60min","total_sed_time_greater_120min","percentile_sed_time_5","percentile_sed_time_25","percentile_sed_time_50","percentile_sed_time_75","percentile_sed_time_95","alpha_sed","prop_sed_time_6_12","prop_sed_time_12_18","prop_sed_time_18_22","sed_time_in_30_and_60","sed_num_in_30_and_60")
res <- list(call=object$call,
Table=TAB)
class(res) <- "summary.Sedentary"
res
}
YukunZhang/PAactivPAL documentation built on May 10, 2019, 1:11 a.m.
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Sedentary_summary=function (final_dat,bed_time,takeoff_time) {
options(digits = 10)
if(missing(takeoff_time)){
final_dat=clean_time(final_dat)}else{
final_dat=clean_time(final_dat,takeoff_time)
}
if(missing(bed_time)){
table1=c()
# record.getup<-bed_time
# ###
# record.getup.time<-c()
# record.sleep.time<-c()
# for (kk in 1:nrow(record.getup))
# {
# temp.getup.time<-as.numeric(as.POSIXlt(strptime(as.character(paste(as.character(record.getup[kk,3] ),as.character(record.getup[kk,4]))),"%m/%d/%Y %H:%M:%S"))+2209136400)/24/60/60
# record.getup.time<-c(record.getup.time,temp.getup.time)
# temp.sleep.time<-as.numeric(as.POSIXlt(strptime(as.character(paste(as.character(record.getup[kk,5] ),as.character(record.getup[kk,6]))),"%m/%d/%Y %H:%M:%S"))+2209136400)/24/60/60
# record.sleep.time<-c(record.sleep.time,temp.sleep.time)
# }
###################################################
for (i in unique(final_dat$new_date)){
temp_mat_oneday=final_dat[final_dat$new_date==i,]
# temp_mat_oneday<-temp_mat_oneday[temp_mat_oneday[,1]>record.getup.time[ll] &temp_mat_oneday[,1]<record.sleep.time[ll], ]
time_char<-as.POSIXlt(i*24*60*60, origin = ISOdatetime(1899,12,30,0,0,0))
month<-as.numeric(format(time_char,"%m"))
day<-as.numeric(format(time_char,"%d"))
year<-as.numeric(format(time_char,"%Y"))
dayofweek<-as.numeric(format(time_char,"%w"))
weekday_or_weekend<- ifelse( dayofweek!=0 & dayofweek!=6,1,0)
hour_char<-as.numeric(format(as.POSIXlt(temp_mat_oneday$date_time*24*60*60, origin = ISOdatetime(1899,12,30,0,0,0)),"%H"))
temp_sed<-subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0)$Interval
length_temp_sed<-length(temp_sed)
num_changes_from_sed_to_non_sed<- length_temp_sed
sed_hour<- sum(temp_sed) /60/60
length_temp_sed<-length(temp_sed)
num_changes_from_sed_to_non_sed<- length_temp_sed
total_sed_time<-sed_hour
#total_number_of_sed_bouts<-num_changes_from_sed_to_non_sed
mean_sed_bout_length<- mean(temp_sed) /60/60
prop_of_sed_time_greater_20min<- 100*length(temp_sed[temp_sed>20*60])/length_temp_sed
prop_of_sed_time_greater_60min<- 100*length(temp_sed[temp_sed>60*60])/length_temp_sed
prop_of_sed_time_greater_120min<- 100*length(temp_sed[temp_sed>120*60])/length_temp_sed
total_sed_time_greater_20min<- sum(temp_sed[temp_sed>20*60])/60/60
total_sed_time_greater_60min<- sum(temp_sed[temp_sed>60*60])/60/60
total_sed_time_greater_120min<- sum(temp_sed[temp_sed>120*60])/60/60
sed_time_in_30_and_60=sum(temp_sed[temp_sed>=30*60 & temp_sed<=60*60])/60
sed_num_in_30_and_60=length(temp_sed[temp_sed>=30*60 & temp_sed<=60*60])
quantile_temp<-quantile(temp_sed, probs = c(0.05,0.25,0.5,0.75,0.95))/60/60
percentile_sed_time_5<- quantile_temp[1]
percentile_sed_time_25<- quantile_temp[2]
percentile_sed_time_50<- quantile_temp[3]
percentile_sed_time_75<- quantile_temp[4]
percentile_sed_time_95<- quantile_temp[5]
alpha_sed<- 1+ 1/mean(log(temp_sed/ min(temp_sed)))
#gini_index_sed<- gini(temp_sed)
prop_sed_time_6_12<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=6 & hour_char<12)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=6 & hour_char<12)$Interval)+0.0001) ###prevent this value is zero
prop_sed_time_12_18<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=12 & hour_char<18)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=12 & hour_char<18)$Interval)+0.0001)
prop_sed_time_18_22<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=18 & hour_char<22)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=18 & hour_char<22)$Interval)+0.0001)
break_per_day<-num_changes_from_sed_to_non_sed
break_rate<-break_per_day/sed_hour
table<- cbind(year,month,day,dayofweek,weekday_or_weekend,break_per_day,break_rate,mean_sed_bout_length,prop_of_sed_time_greater_20min,prop_of_sed_time_greater_60min,prop_of_sed_time_greater_120min,total_sed_time_greater_20min,total_sed_time_greater_60min,total_sed_time_greater_120min,percentile_sed_time_5,percentile_sed_time_25,percentile_sed_time_50,percentile_sed_time_75,percentile_sed_time_95,alpha_sed,prop_sed_time_6_12,prop_sed_time_12_18,prop_sed_time_18_22,sed_time_in_30_and_60,sed_num_in_30_and_60)
row.names(table)=NULL
table1=rbind(table1,table)
#print(table1)
}
}else{
table1=c()
record.getup<-bed_time
###
record.getup.time<-c()
record.sleep.time<-c()
for (kk in 1:nrow(record.getup))
{
temp.getup.time<-as.numeric(as.POSIXlt(strptime(as.character(paste(as.character(record.getup[kk,3] ),as.character(record.getup[kk,4]))),"%m/%d/%Y %H:%M:%S"))+2209136400)/24/60/60
record.getup.time<-c(record.getup.time,temp.getup.time)
temp.sleep.time<-as.numeric(as.POSIXlt(strptime(as.character(paste(as.character(record.getup[kk,5] ),as.character(record.getup[kk,6]))),"%m/%d/%Y %H:%M:%S"))+2209136400)/24/60/60
record.sleep.time<-c(record.sleep.time,temp.sleep.time)
}
###################################################
ll=0 #count for getup time
for (i in unique(final_dat$new_date)){
ll=ll+1
temp_mat_oneday=final_dat[final_dat$new_date==i,]
temp_mat_oneday<-temp_mat_oneday[temp_mat_oneday[,1]>record.getup.time[ll] &temp_mat_oneday[,1]<record.sleep.time[ll], ]
time_char<-as.POSIXlt(i*24*60*60, origin = ISOdatetime(1899,12,30,0,0,0))
month<-as.numeric(format(time_char,"%m"))
day<-as.numeric(format(time_char,"%d"))
year<-as.numeric(format(time_char,"%Y"))
dayofweek<-as.numeric(format(time_char,"%w"))
weekday_or_weekend<- ifelse( dayofweek!=0 & dayofweek!=6,1,0)
hour_char<-as.numeric(format(as.POSIXlt(temp_mat_oneday$date_time*24*60*60, origin = ISOdatetime(1899,12,30,0,0,0)),"%H"))
temp_sed<-subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0)$Interval
length_temp_sed<-length(temp_sed)
num_changes_from_sed_to_non_sed<- length_temp_sed
sed_hour<- sum(temp_sed) /60/60
length_temp_sed<-length(temp_sed)
num_changes_from_sed_to_non_sed<- length_temp_sed
total_sed_time<-sed_hour
#total_number_of_sed_bouts<-num_changes_from_sed_to_non_sed
mean_sed_bout_length<- mean(temp_sed) /60/60
prop_of_sed_time_greater_20min<- 100*length(temp_sed[temp_sed>20*60])/length_temp_sed
prop_of_sed_time_greater_60min<- 100*length(temp_sed[temp_sed>60*60])/length_temp_sed
prop_of_sed_time_greater_120min<- 100*length(temp_sed[temp_sed>120*60])/length_temp_sed
total_sed_time_greater_20min<- sum(temp_sed[temp_sed>20*60])/60/60
total_sed_time_greater_60min<- sum(temp_sed[temp_sed>60*60])/60/60
total_sed_time_greater_120min<- sum(temp_sed[temp_sed>120*60])/60/60
sed_time_in_30_and_60=sum(temp_sed[temp_sed>=30*60 & temp_sed<=60*60])/60
sed_num_in_30_and_60=length(temp_sed[temp_sed>=30*60 & temp_sed<=60*60])
quantile_temp<-quantile(temp_sed, probs = c(0.05,0.25,0.5,0.75,0.95))/60/60
percentile_sed_time_5<- quantile_temp[1]
percentile_sed_time_25<- quantile_temp[2]
percentile_sed_time_50<- quantile_temp[3]
percentile_sed_time_75<- quantile_temp[4]
percentile_sed_time_95<- quantile_temp[5]
alpha_sed<- 1+ 1/mean(log(temp_sed/ min(temp_sed)))
#gini_index_sed<- gini(temp_sed)
prop_sed_time_6_12<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=6 & hour_char<12)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=6 & hour_char<12)$Interval)+0.0001) ###prevent this value is zero
prop_sed_time_12_18<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=12 & hour_char<18)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=12 & hour_char<18)$Interval)+0.0001)
prop_sed_time_18_22<- 100*sum(subset(temp_mat_oneday,temp_mat_oneday$ActivityCode==0 & hour_char>=18 & hour_char<22)$Interval) /(sum(subset(temp_mat_oneday,hour_char>=18 & hour_char<22)$Interval)+0.0001)
break_per_day<-num_changes_from_sed_to_non_sed
break_rate<-break_per_day/sed_hour
table<- cbind(year,month,day,dayofweek,weekday_or_weekend,break_per_day,break_rate,mean_sed_bout_length,prop_of_sed_time_greater_20min,prop_of_sed_time_greater_60min,prop_of_sed_time_greater_120min,total_sed_time_greater_20min,total_sed_time_greater_60min,total_sed_time_greater_120min,percentile_sed_time_5,percentile_sed_time_25,percentile_sed_time_50,percentile_sed_time_75,percentile_sed_time_95,alpha_sed,prop_sed_time_6_12,prop_sed_time_12_18,prop_sed_time_18_22,sed_time_in_30_and_60,sed_num_in_30_and_60)
row.names(table)=NULL
table1=rbind(table1,table)
#print(table1)
}
}
#colnames(table1)<- c("year","month","day","dayofweek","weekday_or_weekend","break_per_day","break_rate","mean_sed_bout_length" ,"prop_of_sed_time_greater_20min","prop_of_sed_time_greater_60min","prop_of_sed_time_greater_120min","total_sed_time_greater_20min","total_sed_time_greater_60min","total_sed_time_greater_120min","percentile_sed_time_5","percentile_sed_time_25","percentile_sed_time_50","percentile_sed_time_75","percentile_sed_time_95","alpha_sed","prop_sed_time_6_12","prop_sed_time_12_18","prop_sed_time_18_22","sed_time_in_30_and_60","sed_num_in_30_and_60")
list(year=table1[,1],month=table1[,2], day=table1[,3],dayofweek=table1[,4],weekday_or_weekend=table1[,5], break_per_day=table1[,6],break_rate=table1[,7], mean_sed_bout_length=table1[,8], prop_of_sed_time_greater_20min=table1[,9], prop_of_sed_time_greater_60min=table1[,10],prop_of_sed_time_greater_120min=table1[,11],total_sed_time_greater_20min=table1[,12],total_sed_time_greater_60min=table1[,13],total_sed_time_greater_120min=table1[,14],percentile_sed_time_5=table1[,15],percentile_sed_time_25=table1[,16],percentile_sed_time_50=table1[,17],percentile_sed_time_75=table1[,18],percentile_sed_time_95=table1[,19],alpha_sed=table1[,20],prop_sed_time_6_12=table1[,21],prop_sed_time_12_18=table1[,22],prop_sed_time_18_22=table1[,23],sed_time_in_30_and_60=table1[,24],sed_num_in_30_and_60=table1[,25])
# out=list( total_number_of_activity_bouts=total_number_of_activity_bouts,mean_activity_bout_length=mean_activity_bout_length,prop_of_activity_time_greater_5min=prop_of_activity_time_greater_5min,prop_of_activity_time_greater_10min=prop_of_activity_time_greater_10min,prop_of_activity_time_greater_30min=prop_of_activity_time_greater_30min,total_activity_time_greater_5min=total_activity_time_greater_5min,total_activity_time_greater_10min=total_activity_time_greater_10min,total_activity_time_greater_30min=total_activity_time_greater_30min,percentile_activity_time_5=percentile_activity_time_5,percentile_activity_time_25=percentile_activity_time_25,percentile_activity_time_50=percentile_activity_time_50,percentile_activity_time_75=percentile_activity_time_75,percentile_activity_time_95=percentile_activity_time_95,alpha_activity=alpha_activity,stepping_to_standing_ratio=stepping_to_standing_ratio,table=table)
#return(table1)
}
#' Sedentary Physical Activity Summary
#'
#' Summarize sedentary features using proportions and percentiles
#' @param final_dat Raw event file, will be cleaned in this function. Event file is required for this function.
#' @param takeoff_time Take on and off time log, reported by participants. Log is not required for this function.
#' @param bed_time Sleep and wake up time log, reported by participants. Log is not required for this function.
#' @return \code{Year} The calendar year of recorded event
#' @return \code{Month} The calendar month of recorded event
#' @return \code{Day} The calendar day of recorded event
#' @return \code{Dayofweek} The day of that week
#' @return \code{Weekday_or_weekend} The recored event date is a weekday or weekend (0 for weekday, 1 for weekend)
#' @return \code{break_per_day} Number of interrupting sedentary behavior during the given period of time
#' @return \code{break_rate} Rate of interrupting sedentary behavior
#' @return \code{mean_sed_bout_length} Mean sedentary bout length. It is the average of the duration time for all sedentary activities
#' @return \code{prop_of_sed_time_greater_20min} Proportions of sedentary bout greater than 20 minutes
#' @return \code{prop_of_sed_time_greater_60min} Proportions of sedentary bout greater than 60 minutes
#' @return \code{prop_of_sed_time_greater_120min} Proportions of sedentary bout greater than 120 minutes
#' @return \code{total_sed_time_greater_20min} Total sedentary time greater than 20 minutes
#' @return \code{total_sed_time_greater_60min} Total sedentary time greater than 60 minutes
#' @return \code{total_sed_time_greater_120min} Total sedentary time greater than 120 minutes
#' @return \code{percentile_sed_time_5} 5\% Percentile of sedentary time
#' @return \code{percentile_sed_time_25} 25\% Percentile of sedentary time
#' @return \code{percentile_sed_time_50} 50\% Percentile of sedentary time
#' @return \code{percentile_sed_time_75} 75\% Percentile of sedentary time
#' @return \code{percentile_sed_time_95} 95\% Percentile of sedentary time
#' @return \code{alpha_sed} alpha of sedentary time, see details
#' @return \code{prop_sed_time_6_12} Proportions of sedentary time between 6:00-12:00
#' @return \code{prop_sed_time_12_18} Proportions of sedentary time between 12:00-18:00
#' @return \code{prop_sed_time_18_22} Proportions of sedentary time between 18:00-22:00
#' @return \code{sed_time_in_30_and_60} Minutes Spent in Sitting/Lying Bouts (at least 30 and less 60 minutes in duration)
#' @return \code{sed_num_in_30_and_60} Number of in Sitting/Lying Bouts (at least 30 and less 60 minutes in duration)
#' @details Proportions of sedentary bout greater than 20/60/120 minutes is the ratio of the number of sedentary bouts greater than 20/60/120 minutes to the total number of sedentary recordings.
#' @details Total sedentary time greater than 20/60/120 minutes is the summation of the sedentary durations which are greater than 20/60/120 minutes.
#' @details To calculate 5\%/25\%/75\%/95\% percentile of sedentary time, all of the recorded sedentary durations are listed and R function \emph{quantile} is used to find the percentiles.
#' @details alpha_sed is defined by \code{1+1/M}, where \code{M} is the average of \code{log(sedentary bout length /minimum sedentary bout length)}.
#' @details Proportions of sedentary time between 6:00-12:00/12:00-18:00/18:00-22:00 is the ratio of the sedentary durations to the total activity durations between 6:00-12:00/12:00-18:00/18:00-22:00.
#' @importFrom stats quantile
#' @examples
#' \donttest{r2=Sedentary(sample_event,sample_bed_time,sample_takeon_log)}
#' \donttest{summary(r2)}
#' @export
#'
Sedentary=function(final_dat,bed_time,takeoff_time) UseMethod("Sedentary")
#' @export
Sedentary.default=function(final_dat,bed_time,takeoff_time)
{
out=Sedentary_summary(final_dat,bed_time,takeoff_time)
out$call=match.call()
class(out)="Sedentary"
out
}
#' @export
print.Sedentary=function(x,...)
{cat("Call:\n")
print(x$call)
}
#' @export
summary.Sedentary=function(object,...)
{
TAB=cbind(object$year,object$month,object$day,object$dayofweek,object$weekday_or_weekend,object$break_per_day,object$break_rate,object$mean_sed_bout_length,object$prop_of_sed_time_greater_20min,object$prop_of_sed_time_greater_60min,object$prop_of_sed_time_greater_120min,object$total_sed_time_greater_20min,object$total_sed_time_greater_60min,object$total_sed_time_greater_120min,object$percentile_sed_time_5,object$percentile_sed_time_25,object$percentile_sed_time_50,object$percentile_sed_time_75,object$percentile_sed_time_95,object$alpha_sed,object$prop_sed_time_6_12,object$prop_sed_time_12_18,object$prop_sed_time_18_22,object$sed_time_in_30_and_60,object$sed_num_in_30_and_60)
colnames(TAB)=c("Year","Month","Day","Dayofweek","Weekday_or_weekend","break_per_day","break_rate","mean_sed_bout_length" ,"prop_of_sed_time_greater_20min","prop_of_sed_time_greater_60min","prop_of_sed_time_greater_120min","total_sed_time_greater_20min","total_sed_time_greater_60min","total_sed_time_greater_120min","percentile_sed_time_5","percentile_sed_time_25","percentile_sed_time_50","percentile_sed_time_75","percentile_sed_time_95","alpha_sed","prop_sed_time_6_12","prop_sed_time_12_18","prop_sed_time_18_22","sed_time_in_30_and_60","sed_num_in_30_and_60")
res <- list(call=object$call,
Table=TAB)
class(res) <- "summary.Sedentary"
res
}
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